Abstract

In this study, a Monte Carlo simulation approach is proposed for mapping landslide hazard in terms of return period, in order to account for both rainfall high frequency variability and antecedent precipitation that determine initial conditions. The Monte Carlo approach combines a stochastic rainfall generator with a physically-based landslide triggering model. More in detail, the Monte Carlo simulation methodology comprises the following elements: (a) a seasonal Neyman-Scott Rectangular Pulses (NSRP) model to generate 1,000-years of synthetic hourly point rainfall data; (b) a module for rainfall event identification and separation from dry intervals; (c) the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model, version 2 (Baum et al. 2008, 2010) to simulate landslide triggering by rainfall infiltration, integrated with (d) a water table recession (WTR) model aimed at computing the initial water table height to be used in simulating rainfall events with event-based model TRIGRS.

An application of the method is carried out to map landslide triggering hazard in the Loco catchment, located in the Peloritani Mountains, Sicily, Italy, an area highly prone to landslide risk.

Results show that return period estimation may be significantly affected by both rainfall high-frequency variability and antecedent precipitation. Comparison with results obtained from the IDF-based procedure, shows that the latter widely-used approach generally leads to an overestimation of the return period of landslide triggering, i.e. a non conservative estimation of landslide hazard. Hence the IDF-based approach should be properly modified to account for at least the effect of antecedent precipitation.